Neutron crystallography is a powerful technique for directly visualizing the location of hydrogen atoms in biological macromolecules. This information has provided new key insights into enzyme mechanism, ligand binding and hydration. However, despite the importance of this information, the application of neutron crystallography in biology has been limited by the relatively low flux of available neutron beams and the large incoherent neutron scattering from hydrogen, both of which contribute to weak diffraction data with relatively low signal-to-background ratios. We have developed a method to fit weak data based on 3D profile fitting in reciprocal space of Bragg peaks by an Ikeda-Carpenter function with a bivariate Gaussian. When applied to data collected from three different proteins, 3D profile fitting produces improved merging statistics, improved Rfree factors, extended resolutions and improved nuclear density maps. Importantly, additional features are revealed in nuclear density maps that may provide additional scientific information. These results suggest that 3D profile fitting will help extend the capabilities of neutron macromolecular crystallography.